Abstract

The recent rapid development of artificial intelligence (AI) is expected to transform how governments work by enhancing the quality of decision-making. Despite rising expectations and the growing use of AI by governments, scholarly research on AI applications in public administration has lagged. In this study, we fill gaps in the current literature on the application of machine learning (ML) algorithms with a focus on revenue forecasting by local governments. Specifically, we explore how different ML models perform on predicting revenue for local governments and compare the relative performance of revenue forecasting by traditional forecasters and several ML algorithms. Our findings reveal that traditional statistical forecasting methods outperform ML algorithms overall, while one of ML algorithms, KNN, is more effective in predicting property tax revenue. This result is particularly salient for public managers in local governments to handle foreseeable fiscal challenges through more accurate predictions of revenue.

摘要

最近人工智能 (AI) 的快速发展有望通过提高决策质量来改变政府的工作方式。尽管人们对人工智能的期望越来越高，政府越来越多地使用人工智能，但关于人工智能在公共管理中的应用的学术研究仍然滞后。在这项研究中，我们填补了当前文献中关于机器学习 (ML) 算法应用的空白，重点是地方政府的收入预测。具体来说，我们探讨了不同的机器学习模型在预测地方政府收入方面的表现，并比较了传统预测器和几种机器学习算法在收入预测方面的相对表现。我们的研究结果表明，传统的统计预测方法总体上优于 ML 算法，而 ML 算法之一，KNN，更有效地预测财产税收入。这一结果对于地方政府的公共管理者通过更准确的收入预测来应对可预见的财政挑战尤为重要。